The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This paper investigates the psychological processes of trust calibration in highly automated vehicles (SAE Levels 3 and 4), addressing the critical need for drivers to maintain a calibrated level of trust that matches system capabilities. The authors propose a theoretical model of dynamic trust calibration, synthesizing existing frameworks to explain how trust evolves from initial learned trust (based on prior information) to dynamic learned trust (updated through real-time interaction). The study aims to determine how trust develops during early system use, specifically examining the impacts of system-initiated take-over requests (TORs), system malfunctions, and system transparency. The research employed two driving simulator experiments involving 31 participants in Study 1 and additional groups in Study 2. In Study 1, participants engaged in a dual-task paradigm, driving a simulated highway while playing a secondary game, to ensure naturalistic engagement. Trust, reliability, predictability, and competence were measured repeatedly using self-report questionnaires. Study 1 utilized a one-factorial mixed design where one group experienced a system malfunction (MF+) and another did not (MF–). Both groups encountered TORs. Study 2 introduced a 2 × 2 between-subjects design to manipulate system transparency, providing some participants with prior information about system limitations and malfunction consequences. Results indicated that trust progressively increased during error-free interactions, supporting the hypothesis that reliable system performance builds trust over time. However, both TORs and system malfunctions caused temporary decreases in trust. Crucially, trust was reestablished following these events as long as subsequent system interactions remained error-free. In Study 2, the high-transparency condition mitigated the negative impact of malfunctions; participants who received prior information about system limitations and safe modes did not exhibit the temporary decline in trust observed in low-transparency conditions. Additionally, beliefs about system characteristics were found to significantly correlate with trust levels, confirming that subjective attributions drive trust calibration. The findings underscore that trust is not static but dynamically calibrated through continuous feedback loops involving system performance and user beliefs. The study concludes that while errors and limitations inevitably cause temporary trust dips, these can be recovered through sustained reliable performance. More importantly, providing transparent information about system capabilities and limitations prior to use can prevent trust reduction during malfunctions. These results imply that human-machine interface designs and tutorials for automated vehicles must prioritize transparency to facilitate appropriate trust calibration, thereby enhancing safety and efficiency in human-automation interaction.
Key finding
Providing transparent information about system limitations and malfunction consequences prior to driving prevents the temporary decline in trust that typically follows a system malfunction.
Methodology
simulator
Sample size: 31
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | openalex | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-07 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- trust calibration
- automation
- trust in automation foundations
- automation surprise
- takeover transitions
- mode awareness
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: self report data
- Theoretical Contribution: conceptual framework